Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

• Define a Function that Makes a Graph
• Question 1: Use yfinance to Extract Stock Data
• Question 2: Use Webscraping to Extract Tesla Revenue Data
• Question 3: Use yfinance to Extract Stock Data
• Question 4: Use Webscraping to Extract GME Revenue Data
• Question 5: Plot Tesla Stock Graph
• Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

---

Note:- If you are working Locally using anaconda, please uncomment the following code and execute it.

#!pip install yfinance==0.2.38
#!pip install pandas==2.2.2
#!pip install nbformat

# !pip install yfinance
# !pip install bs4
# !pip install nbformat

import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Define Graphing Function

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.

Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.

Question 1: Use yfinance to Extract Stock Data

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

tesla = yf.Ticker('TSLA')

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.

tesla_data = tesla.history(period="max")

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

tesla_data.reset_index(inplace=True)

tesla_data.head()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2010-06-29 00:00:00-04:00	1.266667	1.666667	1.169333	1.592667	281494500	0.0	0.0
1	2010-06-30 00:00:00-04:00	1.719333	2.028000	1.553333	1.588667	257806500	0.0	0.0
2	2010-07-01 00:00:00-04:00	1.666667	1.728000	1.351333	1.464000	123282000	0.0	0.0
3	2010-07-02 00:00:00-04:00	1.533333	1.540000	1.247333	1.280000	77097000	0.0	0.0
4	2010-07-06 00:00:00-04:00	1.333333	1.333333	1.055333	1.074000	103003500	0.0	0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

html_data = requests.get('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm').text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser. Make sure to use the html_data with the content parameter as follow html_data.content .

soup = BeautifulSoup(html_data)

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Step-by-step instructions

Here are the step-by-step instructions:

1. Find All Tables: Start by searching for all HTML tables on a webpage using `soup.find_all('table')`.
2. Identify the Relevant Table: then loops through each table. If a table contains the text “Tesla Quarterly Revenue,”, select that table.
3. Initialize a DataFrame: Create an empty Pandas DataFrame called `tesla_revenue` with columns “Date” and “Revenue.”
4. Loop Through Rows: For each row in the relevant table, extract the data from the first and second columns (date and revenue).
5. Clean Revenue Data: Remove dollar signs and commas from the revenue value.
6. Add Rows to DataFrame: Create a new row in the DataFrame with the extracted date and cleaned revenue values.
7. Repeat for All Rows: Continue this process for all rows in the table.

Click here if you need help locating the table

    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

We are focusing on quarterly revenue in the lab.
> Note: Instead of using the deprecated pd.append() method, consider using pd.concat([df, pd.DataFrame], ignore_index=True).

table = soup.find_all('table')[1]

tesla_dict = {'Date':[], 'Revenue':[]}

for row in table.find_all('tr')[1:]:
    cols = row.find_all('td')
    tesla_dict['Date'].append(cols[0].text)
    tesla_dict['Revenue'].append(cols[1].text)

tesla_revenue = pd.DataFrame(columns=['Date', 'Revenue'])
tesla_revenue = pd.concat([tesla_revenue, pd.DataFrame(tesla_dict)])

Execute the following line to remove the comma and dollar sign from the Revenue column.

tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"", regex=True)

Execute the following lines to remove an null or empty strings in the Revenue column.

tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"", regex=True)
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

tesla_revenue.tail()

	Date	Revenue
48	2010-09-30	31
49	2010-06-30	28
50	2010-03-31	21
52	2009-09-30	46
53	2009-06-30	27

Question 3: Use yfinance to Extract Stock Data

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

GameStop = yf.Ticker('GME')

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.

gme_data = GameStop.history(period='max')
gme_data

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2002-02-13 00:00:00-05:00	1.620128	1.693350	1.603296	1.691666	76216000	0.0	0.0
2002-02-14 00:00:00-05:00	1.712707	1.716074	1.670626	1.683250	11021600	0.0	0.0
2002-02-15 00:00:00-05:00	1.683250	1.687458	1.658002	1.674834	8389600	0.0	0.0
2002-02-19 00:00:00-05:00	1.666418	1.666418	1.578047	1.607504	7410400	0.0	0.0
2002-02-20 00:00:00-05:00	1.615920	1.662210	1.603296	1.662210	6892800	0.0	0.0
...	...	...	...	...	...	...	...
2024-09-09 00:00:00-04:00	23.240000	25.020000	23.160000	24.250000	14064700	0.0	0.0
2024-09-10 00:00:00-04:00	24.770000	24.799999	23.129999	23.450001	19175500	0.0	0.0
2024-09-11 00:00:00-04:00	20.820000	21.090000	19.309999	20.639999	28921500	0.0	0.0
2024-09-12 00:00:00-04:00	20.469999	20.709999	19.990000	20.400000	9567500	0.0	0.0
2024-09-13 00:00:00-04:00	20.490000	20.920000	20.340000	20.650000	8037100	0.0	0.0

5685 rows × 7 columns

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

gme_data.reset_index(inplace=True)

Question 4: Use Webscraping to Extract GME Revenue Data

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.

html_data_2 = requests.get('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html').text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

soup = BeautifulSoup('html_data_2')

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

Note: Use the method similar to what you did in question 2.

Click here if you need help locating the table

    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

gs_dict = {'Date':[], 'Revenue':[]}

for row in table.find_all('tr')[1:]:
    cols = row.find_all('td')
    gs_dict['Date'].append(cols[0].text)
    gs_dict['Revenue'].append(cols[1].text)

gme_revenue = pd.DataFrame(columns=['Date', 'Revenue'])
gme_revenue = pd.concat([tesla_revenue, pd.DataFrame(tesla_dict)])

gme_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"", regex=True)
gme_revenue.dropna(inplace=True)

gme_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
gme_revenue

	Date	Revenue
0	2022-09-30	21454
1	2022-06-30	16934
2	2022-03-31	18756
3	2021-12-31	17719
4	2021-09-30	13757
5	2021-06-30	11958
6	2021-03-31	10389
7	2020-12-31	10744
8	2020-09-30	8771
9	2020-06-30	6036
10	2020-03-31	5985
11	2019-12-31	7384
12	2019-09-30	6303
13	2019-06-30	6350
14	2019-03-31	4541
15	2018-12-31	7226
16	2018-09-30	6824
17	2018-06-30	4002
18	2018-03-31	3409
19	2017-12-31	3288
20	2017-09-30	2985
21	2017-06-30	2790
22	2017-03-31	2696
23	2016-12-31	2285
24	2016-09-30	2298
25	2016-06-30	1270
26	2016-03-31	1147
27	2015-12-31	1214
28	2015-09-30	937
29	2015-06-30	955
30	2015-03-31	940
31	2014-12-31	957
32	2014-09-30	852
33	2014-06-30	769
34	2014-03-31	621
35	2013-12-31	615
36	2013-09-30	431
37	2013-06-30	405
38	2013-03-31	562
39	2012-12-31	306
40	2012-09-30	50
41	2012-06-30	27
42	2012-03-31	30
43	2011-12-31	39
44	2011-09-30	58
45	2011-06-30	58
46	2011-03-31	49
47	2010-12-31	36
48	2010-09-30	31
49	2010-06-30	28
50	2010-03-31	21
52	2009-09-30	46
53	2009-06-30	27

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

gme_revenue.tail()

	Date	Revenue
48	2010-09-30	31
49	2010-06-30	28
50	2010-03-31	21
52	2009-09-30	46
53	2009-06-30	27

Question 5: Plot Tesla Stock Graph

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.

Hint

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.

make_graph(tesla_data, tesla_revenue, 'tesla')

Question 6: Plot GameStop Stock Graph

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

Hint

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`

make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

© IBM Corporation 2020. All rights reserved.

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